Overview

Dataset statistics

Number of variables19
Number of observations26860
Missing cells0
Missing cells (%)0.0%
Duplicate rows625
Duplicate rows (%)2.3%
Total size in memory3.9 MiB
Average record size in memory152.0 B

Variable types

Numeric15
Categorical4

Alerts

Dataset has 625 (2.3%) duplicate rowsDuplicates
Temperature is highly overall correlated with Feels Like and 1 other fieldsHigh correlation
Feels Like is highly overall correlated with Temperature and 1 other fieldsHigh correlation
Humidity is highly overall correlated with VisibilityHigh correlation
Clouds is highly overall correlated with Weather MainHigh correlation
Rain 1h is highly overall correlated with Weather SeverityHigh correlation
Snow 1h is highly overall correlated with Weather MainHigh correlation
Departure Gate is highly overall correlated with Arrival IATA CodeHigh correlation
Arrival IATA Code is highly overall correlated with Departure GateHigh correlation
Weather Main is highly overall correlated with Clouds and 3 other fieldsHigh correlation
Weather Severity is highly overall correlated with Rain 1h and 2 other fieldsHigh correlation
Season is highly overall correlated with Temperature and 1 other fieldsHigh correlation
Visibility is highly overall correlated with Humidity and 2 other fieldsHigh correlation
Arrival IATA Code has 535 (2.0%) zerosZeros
Airline Name has 338 (1.3%) zerosZeros
Weather Main has 2791 (10.4%) zerosZeros
Weekday of Departure has 3492 (13.0%) zerosZeros

Reproduction

Analysis started2024-02-21 22:59:20.642726
Analysis finished2024-02-21 23:00:15.383951
Duration54.74 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

Departure Delay (min)
Real number (ℝ)

Distinct234
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4651406 × 10-18
Minimum-0.86281973
Maximum43.977065
Zeros0
Zeros (%)0.0%
Negative16479
Negative (%)61.4%
Memory size210.0 KiB
2024-02-21T18:00:15.520052image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-0.86281973
5-th percentile-0.7734379
Q1-0.44570452
median-0.17755903
Q30.20976223
95-th percentile1.0737866
Maximum43.977065
Range44.839885
Interquartile range (IQR)0.65546675

Descriptive statistics

Standard deviation1.0000186
Coefficient of variation (CV)1.1813373 × 1017
Kurtosis526.8642
Mean8.4651406 × 10-18
Median Absolute Deviation (MAD)0.29793943
Skewness15.764329
Sum-1.9895197 × 10-13
Variance1.0000372
MonotonicityNot monotonic
2024-02-21T18:00:15.710579image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.8628197281 1102
 
4.1%
-0.2371469208 938
 
3.5%
-0.4159105801 836
 
3.1%
-0.3861166369 829
 
3.1%
-0.266940864 810
 
3.0%
-0.4457045233 788
 
2.9%
-0.4754984665 761
 
2.8%
-0.3265287504 747
 
2.8%
0.03099856801 735
 
2.7%
-0.117971148 724
 
2.7%
Other values (224) 18590
69.2%
ValueCountFrequency (%)
-0.8628197281 1102
4.1%
-0.8330257849 83
 
0.3%
-0.8032318417 117
 
0.4%
-0.7734378985 153
 
0.6%
-0.7436439553 193
 
0.7%
-0.7138500121 240
 
0.9%
-0.6840560689 273
 
1.0%
-0.6542621257 331
 
1.2%
-0.6244681825 361
 
1.3%
-0.5946742393 468
1.7%
ValueCountFrequency (%)
43.9770648 1
< 0.1%
42.8746889 1
< 0.1%
42.04045849 1
< 0.1%
35.18785155 1
< 0.1%
31.76154808 1
< 0.1%
24.16409256 1
< 0.1%
22.37645597 1
< 0.1%
19.99294052 1
< 0.1%
19.69500108 1
< 0.1%
14.18312159 1
< 0.1%

Temperature
Real number (ℝ)

HIGH CORRELATION 

Distinct1536
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum-2.4918277
Maximum3.3104091
Zeros0
Zeros (%)0.0%
Negative15014
Negative (%)55.9%
Memory size210.0 KiB
2024-02-21T18:00:15.908089image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-2.4918277
5-th percentile-1.5287662
Q1-0.72084469
median-0.09551107
Q30.59665209
95-th percentile1.7852633
Maximum3.3104091
Range5.8022367
Interquartile range (IQR)1.3174968

Descriptive statistics

Standard deviation1.0000186
Coefficient of variation (CV)nan
Kurtosis0.3826244
Mean0
Median Absolute Deviation (MAD)0.66232855
Skewness0.51350762
Sum1.1368684 × 10-13
Variance1.0000372
MonotonicityNot monotonic
2024-02-21T18:00:16.103602image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.532346329 132
 
0.5%
-1.651684806 113
 
0.4%
-0.255424629 102
 
0.4%
-0.3640226428 90
 
0.3%
-1.054992423 88
 
0.3%
-0.1265390742 85
 
0.3%
-0.3389615627 83
 
0.3%
-0.1874016973 73
 
0.3%
-0.291226172 71
 
0.3%
0.3054662113 69
 
0.3%
Other values (1526) 25954
96.6%
ValueCountFrequency (%)
-2.491827682 4
 
< 0.1%
-2.485860758 5
 
< 0.1%
-2.463186447 14
0.1%
-2.415451057 12
 
< 0.1%
-2.380842898 5
 
< 0.1%
-2.34862151 1
 
< 0.1%
-2.342654586 12
 
< 0.1%
-2.336687662 25
0.1%
-2.32356043 30
0.1%
-2.309239812 28
0.1%
ValueCountFrequency (%)
3.310409054 15
0.1%
3.274607511 9
 
< 0.1%
3.259093509 28
0.1%
3.166009497 27
0.1%
3.164816112 16
0.1%
3.088439487 14
0.1%
3.080085794 15
0.1%
3.077699024 19
0.1%
3.056218099 9
 
< 0.1%
3.055024714 25
0.1%

Feels Like
Real number (ℝ)

HIGH CORRELATION 

Distinct1647
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-8.4651406 × 10-18
Minimum-2.4608667
Maximum3.134223
Zeros0
Zeros (%)0.0%
Negative14539
Negative (%)54.1%
Memory size210.0 KiB
2024-02-21T18:00:16.293688image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-2.4608667
5-th percentile-1.5444208
Q1-0.70104708
median-0.066740705
Q30.6051166
95-th percentile1.7560021
Maximum3.134223
Range5.5950896
Interquartile range (IQR)1.3061637

Descriptive statistics

Standard deviation1.0000186
Coefficient of variation (CV)-1.1813373 × 1017
Kurtosis0.14165613
Mean-8.4651406 × 10-18
Median Absolute Deviation (MAD)0.64749994
Skewness0.41799309
Sum-5.6843419 × 10-14
Variance1.0000372
MonotonicityNot monotonic
2024-02-21T18:00:16.473927image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.00430160792 89
 
0.3%
-1.739279741 77
 
0.3%
-0.9425909407 76
 
0.3%
0.2915155321 69
 
0.3%
-0.04238334089 68
 
0.3%
0.07026947037 68
 
0.3%
-0.3194483632 67
 
0.2%
0.02662919213 65
 
0.2%
-0.6076771775 62
 
0.2%
-0.6066622874 62
 
0.2%
Other values (1637) 26157
97.4%
ValueCountFrequency (%)
-2.460866667 5
 
< 0.1%
-2.373586111 24
0.1%
-2.326901162 10
 
< 0.1%
-2.319796931 21
0.1%
-2.312692699 4
 
< 0.1%
-2.305588468 12
 
< 0.1%
-2.289350225 46
0.2%
-2.282245993 5
 
< 0.1%
-2.270067311 9
 
< 0.1%
-2.240635496 12
 
< 0.1%
ValueCountFrequency (%)
3.134222958 15
0.1%
3.113925155 28
0.1%
3.086523119 9
 
< 0.1%
3.074344437 16
0.1%
3.028674379 27
0.1%
2.987063881 25
0.1%
2.98401921 15
0.1%
2.969810747 14
0.1%
2.960676736 9
 
< 0.1%
2.958646955 17
0.1%

Pressure
Real number (ℝ)

Distinct78
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0873981 × 10-15
Minimum-3.4574709
Maximum2.1988041
Zeros0
Zeros (%)0.0%
Negative12237
Negative (%)45.6%
Memory size210.0 KiB
2024-02-21T18:00:16.673045image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-3.4574709
5-th percentile-1.7020752
Q1-0.62933339
median0.15084247
Q30.63845238
95-th percentile1.6136722
Maximum2.1988041
Range5.656275
Interquartile range (IQR)1.2677858

Descriptive statistics

Standard deviation1.0000186
Coefficient of variation (CV)1.2365146 × 1014
Kurtosis0.53103626
Mean8.0873981 × 10-15
Median Absolute Deviation (MAD)0.58513189
Skewness-0.54417648
Sum2.1668711 × 10-10
Variance1.0000372
MonotonicityNot monotonic
2024-02-21T18:00:16.876718image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3458864357 1571
 
5.8%
0.2483644534 1357
 
5.1%
0.443408418 1255
 
4.7%
-0.3367674405 1074
 
4.0%
0.735974365 1068
 
4.0%
0.1508424711 1040
 
3.9%
-0.4342894228 990
 
3.7%
0.6384523827 980
 
3.6%
0.05332048876 936
 
3.5%
0.5409304004 883
 
3.3%
Other values (68) 15706
58.5%
ValueCountFrequency (%)
-3.457470875 98
0.4%
-3.359948892 80
0.3%
-3.26242691 25
 
0.1%
-3.164904928 37
 
0.1%
-3.067382945 41
0.2%
-2.969860963 29
 
0.1%
-2.872338981 3
 
< 0.1%
-2.774816999 43
0.2%
-2.677295016 66
0.2%
-2.579773034 41
0.2%
ValueCountFrequency (%)
2.1988041 30
 
0.1%
2.101282117 40
 
0.1%
2.003760135 63
 
0.2%
1.906238153 343
1.3%
1.808716171 469
1.7%
1.711194188 266
1.0%
1.613672206 134
 
0.5%
1.516150224 186
 
0.7%
1.418628241 294
1.1%
1.321106259 476
1.8%

Humidity
Real number (ℝ)

HIGH CORRELATION 

Distinct74
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-8.6445288 × 10-16
Minimum-3.3398262
Maximum1.8349776
Zeros0
Zeros (%)0.0%
Negative13225
Negative (%)49.2%
Memory size210.0 KiB
2024-02-21T18:00:17.311535image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-3.3398262
5-th percentile-1.7610725
Q1-0.70857005
median0.080806798
Q30.78247511
95-th percentile1.3964349
Maximum1.8349776
Range5.1748038
Interquartile range (IQR)1.4910452

Descriptive statistics

Standard deviation1.0000186
Coefficient of variation (CV)-1.1568226 × 1015
Kurtosis-0.45840448
Mean-8.6445288 × 10-16
Median Absolute Deviation (MAD)0.78937685
Skewness-0.36909149
Sum-2.2737368 × 10-11
Variance1.0000372
MonotonicityNot monotonic
2024-02-21T18:00:17.699143image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.396434881 1030
 
3.8%
0.1685153365 1016
 
3.8%
-0.006901741166 926
 
3.4%
-0.09461028002 908
 
3.4%
-0.2700273577 907
 
3.4%
0.519349492 887
 
3.3%
0.08080679769 866
 
3.2%
0.3439324143 860
 
3.2%
1.221017803 837
 
3.1%
0.6070580308 828
 
3.1%
Other values (64) 17795
66.3%
ValueCountFrequency (%)
-3.339826218 16
 
0.1%
-3.076700601 8
 
< 0.1%
-2.988992062 26
 
0.1%
-2.901283523 45
0.2%
-2.813574985 52
0.2%
-2.725866446 29
 
0.1%
-2.638157907 11
 
< 0.1%
-2.550449368 16
 
0.1%
-2.462740829 106
0.4%
-2.37503229 101
0.4%
ValueCountFrequency (%)
1.834977575 48
 
0.2%
1.747269036 139
 
0.5%
1.659560497 89
 
0.3%
1.571851958 345
 
1.3%
1.484143419 706
2.6%
1.396434881 1030
3.8%
1.308726342 826
3.1%
1.221017803 837
3.1%
1.133309264 745
2.8%
1.080684141 1
 
< 0.1%

Wind Speed
Real number (ℝ)

Distinct341
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.7721124 × 10-17
Minimum-1.3140799
Maximum5.495108
Zeros0
Zeros (%)0.0%
Negative15913
Negative (%)59.2%
Memory size210.0 KiB
2024-02-21T18:00:17.960900image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-1.3140799
5-th percentile-1.115497
Q1-0.7227441
median-0.32557825
Q30.5040571
95-th percentile2.0927205
Maximum5.495108
Range6.8091879
Interquartile range (IQR)1.2268012

Descriptive statistics

Standard deviation1.0000186
Coefficient of variation (CV)-1.4766716 × 1016
Kurtosis1.9564603
Mean-6.7721124 × 10-17
Median Absolute Deviation (MAD)0.59133583
Skewness1.3727133
Sum-2.9558578 × 10-12
Variance1.0000372
MonotonicityNot monotonic
2024-02-21T18:00:18.169632image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.9213270286 2819
 
10.5%
-0.722744101 2681
 
10.0%
-1.115497002 1761
 
6.6%
-0.5241611735 1742
 
6.5%
-0.3255782459 1179
 
4.4%
0.7291177472 1077
 
4.0%
0.2745834907 844
 
3.1%
0.0495228395 819
 
3.0%
0.5040570959 721
 
2.7%
0.9541783984 717
 
2.7%
Other values (331) 12500
46.5%
ValueCountFrequency (%)
-1.31407993 112
 
0.4%
-1.239059713 10
 
< 0.1%
-1.172865403 8
 
< 0.1%
-1.137561772 10
 
< 0.1%
-1.115497002 1761
6.6%
-1.10225814 42
 
0.2%
-1.075780417 18
 
0.1%
-1.049302693 23
 
0.1%
-1.044889739 9
 
< 0.1%
-1.027237923 16
 
0.1%
ValueCountFrequency (%)
5.495108009 1
 
< 0.1%
4.59045245 13
 
< 0.1%
4.360978845 34
 
0.1%
4.135918193 57
 
0.2%
3.906444588 13
 
< 0.1%
3.681383937 66
 
0.2%
3.451910332 56
 
0.2%
3.22684968 96
0.4%
2.997376075 106
0.4%
2.772315424 178
0.7%

Wind Degree
Real number (ℝ)

Distinct256
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0422704 × 10-16
Minimum-1.7352398
Maximum1.6778605
Zeros0
Zeros (%)0.0%
Negative11214
Negative (%)41.7%
Memory size210.0 KiB
2024-02-21T18:00:18.439768image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-1.7352398
5-th percentile-1.6404315
Q1-1.0905431
median0.36002455
Q30.82458544
95-th percentile1.3934355
Maximum1.6778605
Range3.4131004
Interquartile range (IQR)1.9151285

Descriptive statistics

Standard deviation1.0000186
Coefficient of variation (CV)9.5946175 × 1015
Kurtosis-1.2005299
Mean1.0422704 × 10-16
Median Absolute Deviation (MAD)0.59729257
Skewness-0.40182708
Sum2.5011104 × 10-12
Variance1.0000372
MonotonicityNot monotonic
2024-02-21T18:00:18.712773image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.616007083 2401
 
8.9%
0.8245854391 2281
 
8.5%
0.3979478924 1005
 
3.7%
0.1893695363 909
 
3.4%
1.04264463 906
 
3.4%
-1.517180651 863
 
3.2%
-1.45081481 828
 
3.1%
-1.308602294 823
 
3.1%
-0.02868965428 782
 
2.9%
-1.735239841 754
 
2.8%
Other values (246) 15308
57.0%
ValueCountFrequency (%)
-1.735239841 754
2.8%
-1.725759007 30
 
0.1%
-1.716278172 11
 
< 0.1%
-1.706797338 34
 
0.1%
-1.697316504 24
 
0.1%
-1.687835669 19
 
0.1%
-1.659393166 1
 
< 0.1%
-1.640431497 517
1.9%
-1.60250816 11
 
< 0.1%
-1.593027325 85
 
0.3%
ValueCountFrequency (%)
1.677860533 283
1.1%
1.668379698 15
 
0.1%
1.658898864 5
 
< 0.1%
1.630456361 1
 
< 0.1%
1.620975526 16
 
0.1%
1.583052189 248
0.9%
1.554609686 19
 
0.1%
1.545128851 22
 
0.1%
1.488243845 116
0.4%
1.478763011 10
 
< 0.1%

Clouds
Real number (ℝ)

HIGH CORRELATION 

Distinct118
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.4560481 × 10-16
Minimum-2.2225421
Maximum0.6650775
Zeros0
Zeros (%)0.0%
Negative8588
Negative (%)32.0%
Memory size210.0 KiB
2024-02-21T18:00:18.948658image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-2.2225421
5-th percentile-2.0781611
Q1-0.46109415
median0.6650775
Q30.6650775
95-th percentile0.6650775
Maximum0.6650775
Range2.8876196
Interquartile range (IQR)1.1261717

Descriptive statistics

Standard deviation1.0000186
Coefficient of variation (CV)-4.0716573 × 1015
Kurtosis-0.16994757
Mean-2.4560481 × 10-16
Median Absolute Deviation (MAD)0
Skewness-1.2092328
Sum-5.4569682 × 10-12
Variance1.0000372
MonotonicityNot monotonic
2024-02-21T18:00:19.148293image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.665077497 14702
54.7%
-0.05682740808 829
 
3.1%
-2.049284946 590
 
2.2%
-2.222542123 548
 
2.0%
0.6362013008 385
 
1.4%
-2.193665927 321
 
1.2%
-2.02040875 320
 
1.2%
0.520696516 280
 
1.0%
0.4918203198 269
 
1.0%
0.5495727122 261
 
1.0%
Other values (108) 8355
31.1%
ValueCountFrequency (%)
-2.222542123 548
2.0%
-2.193665927 321
1.2%
-2.164789731 116
 
0.4%
-2.135913535 220
 
0.8%
-2.107037339 128
 
0.5%
-2.078161142 199
 
0.7%
-2.049284946 590
2.2%
-2.02040875 320
1.2%
-1.991532554 132
 
0.5%
-1.962656358 160
 
0.6%
ValueCountFrequency (%)
0.665077497 14702
54.7%
0.6419765401 2
 
< 0.1%
0.6362013008 385
 
1.4%
0.6073251046 212
 
0.8%
0.5784489084 215
 
0.8%
0.5495727122 261
 
1.0%
0.520696516 280
 
1.0%
0.4918203198 269
 
1.0%
0.4629441236 252
 
0.9%
0.4340679274 158
 
0.6%

Rain 1h
Real number (ℝ)

HIGH CORRELATION 

Distinct95
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0499441 × 10-17
Minimum-0.19794087
Maximum14.234522
Zeros0
Zeros (%)0.0%
Negative24902
Negative (%)92.7%
Memory size210.0 KiB
2024-02-21T18:00:19.382282image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-0.19794087
5-th percentile-0.19794087
Q1-0.19794087
median-0.19794087
Q3-0.19794087
95-th percentile0.78477688
Maximum14.234522
Range14.432463
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0000186
Coefficient of variation (CV)1.9802568 × 1016
Kurtosis78.455131
Mean5.0499441 × 10-17
Median Absolute Deviation (MAD)0
Skewness7.8589033
Sum1.3642421 × 10-12
Variance1.0000372
MonotonicityNot monotonic
2024-02-21T18:00:19.568676image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.1979408745 24898
92.7%
0.7847768803 424
 
1.6%
1.26650127 64
 
0.2%
1.478460001 64
 
0.2%
4.368806339 49
 
0.2%
1.343577172 44
 
0.2%
1.728956684 40
 
0.1%
1.632611806 40
 
0.1%
2.152874147 39
 
0.1%
2.364832878 39
 
0.1%
Other values (85) 1159
 
4.3%
ValueCountFrequency (%)
-0.1979408745 24898
92.7%
-0.113157382 1
 
< 0.1%
-0.09774220148 2
 
< 0.1%
-0.08618081613 1
 
< 0.1%
0.01401785692 1
 
< 0.1%
0.03714062762 2
 
< 0.1%
0.05255580809 19
 
0.1%
0.1103627348 4
 
< 0.1%
0.1373393007 1
 
< 0.1%
0.148900686 1
 
< 0.1%
ValueCountFrequency (%)
14.23452184 27
0.1%
11.63321014 16
0.1%
11.28636857 11
 
< 0.1%
9.744850528 1
 
< 0.1%
9.16678126 34
0.1%
7.914297847 8
 
< 0.1%
7.85649092 22
0.1%
7.297690628 13
 
< 0.1%
6.835235214 4
 
< 0.1%
6.449855703 5
 
< 0.1%

Snow 1h
Real number (ℝ)

HIGH CORRELATION 

Distinct86
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8497852 × 10-18
Minimum-0.17908918
Maximum25.003827
Zeros0
Zeros (%)0.0%
Negative25247
Negative (%)94.0%
Memory size210.0 KiB
2024-02-21T18:00:19.773551image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-0.17908918
5-th percentile-0.17908918
Q1-0.17908918
median-0.17908918
Q3-0.17908918
95-th percentile0.67456899
Maximum25.003827
Range25.182916
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0000186
Coefficient of variation (CV)1.2739439 × 1017
Kurtosis113.85882
Mean7.8497852 × 10-18
Median Absolute Deviation (MAD)0
Skewness9.1362461
Sum9.094947 × 10-13
Variance1.0000372
MonotonicityNot monotonic
2024-02-21T18:00:19.975376image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.1790891768 25246
94.0%
0.3900162646 100
 
0.4%
1.385950787 90
 
0.3%
0.2951653577 71
 
0.3%
0.6745689853 64
 
0.2%
4.990285249 56
 
0.2%
1.670503508 52
 
0.2%
1.812779868 51
 
0.2%
0.4374417181 43
 
0.2%
1.623078054 38
 
0.1%
Other values (76) 1049
 
3.9%
ValueCountFrequency (%)
-0.1790891768 25246
94.0%
-0.0557829978 1
 
< 0.1%
0.03906790909 1
 
< 0.1%
0.07700827185 1
 
< 0.1%
0.2761951763 1
 
< 0.1%
0.2951653577 71
 
0.3%
0.3900162646 100
 
0.4%
0.4374417181 43
 
0.2%
0.4848671715 6
 
< 0.1%
0.532292625 9
 
< 0.1%
ValueCountFrequency (%)
25.0038266 1
 
< 0.1%
17.13120133 1
 
< 0.1%
15.70843773 29
0.1%
14.80735411 6
 
< 0.1%
10.4442124 14
0.1%
7.788387003 1
 
< 0.1%
7.551259735 1
 
< 0.1%
7.314132468 22
0.1%
7.124430654 13
< 0.1%
7.029579747 14
0.1%

Departure Gate
Real number (ℝ)

HIGH CORRELATION 

Distinct73
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.714036
Minimum0
Maximum72
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size210.0 KiB
2024-02-21T18:00:20.167134image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q128
median49
Q358
95-th percentile69
Maximum72
Range72
Interquartile range (IQR)30

Descriptive statistics

Standard deviation17.746527
Coefficient of variation (CV)0.40596862
Kurtosis-0.95859068
Mean43.714036
Median Absolute Deviation (MAD)14
Skewness-0.34164141
Sum1174159
Variance314.93922
MonotonicityNot monotonic
2024-02-21T18:00:20.353970image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 4276
 
15.9%
64 737
 
2.7%
39 672
 
2.5%
63 666
 
2.5%
27 650
 
2.4%
26 639
 
2.4%
28 630
 
2.3%
16 622
 
2.3%
67 587
 
2.2%
69 575
 
2.1%
Other values (63) 16806
62.6%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 2
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
4 110
 
0.4%
5 1
 
< 0.1%
6 24
 
0.1%
7 2
 
< 0.1%
8 1
 
< 0.1%
9 568
2.1%
ValueCountFrequency (%)
72 161
 
0.6%
71 343
1.3%
70 396
1.5%
69 575
2.1%
68 509
1.9%
67 587
2.2%
66 528
2.0%
65 570
2.1%
64 737
2.7%
63 666
2.5%

Arrival IATA Code
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct68
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.346054
Minimum0
Maximum67
Zeros535
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size210.0 KiB
2024-02-21T18:00:20.547612image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q117
median33
Q356
95-th percentile65
Maximum67
Range67
Interquartile range (IQR)39

Descriptive statistics

Standard deviation20.821909
Coefficient of variation (CV)0.58908724
Kurtosis-1.3487878
Mean35.346054
Median Absolute Deviation (MAD)19
Skewness-0.054139381
Sum949395
Variance433.5519
MonotonicityNot monotonic
2024-02-21T18:00:20.726670image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65 2662
 
9.9%
23 1947
 
7.2%
56 1620
 
6.0%
14 1106
 
4.1%
51 1057
 
3.9%
33 899
 
3.3%
52 883
 
3.3%
49 799
 
3.0%
7 700
 
2.6%
15 628
 
2.3%
Other values (58) 14559
54.2%
ValueCountFrequency (%)
0 535
2.0%
1 501
1.9%
2 124
 
0.5%
3 208
 
0.8%
4 626
2.3%
5 243
 
0.9%
6 206
 
0.8%
7 700
2.6%
8 569
2.1%
9 111
 
0.4%
ValueCountFrequency (%)
67 112
 
0.4%
66 117
 
0.4%
65 2662
9.9%
64 255
 
0.9%
63 178
 
0.7%
62 473
 
1.8%
61 283
 
1.1%
60 613
 
2.3%
59 115
 
0.4%
58 220
 
0.8%

Airline Name
Real number (ℝ)

ZEROS 

Distinct107
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.251824
Minimum0
Maximum106
Zeros338
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size210.0 KiB
2024-02-21T18:00:20.932305image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q13
median26
Q378
95-th percentile99
Maximum106
Range106
Interquartile range (IQR)75

Descriptive statistics

Standard deviation37.868237
Coefficient of variation (CV)0.94078312
Kurtosis-1.442004
Mean40.251824
Median Absolute Deviation (MAD)23
Skewness0.45882603
Sum1081164
Variance1434.0034
MonotonicityNot monotonic
2024-02-21T18:00:21.146148image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 6276
23.4%
99 3128
 
11.6%
13 2114
 
7.9%
59 1365
 
5.1%
4 1217
 
4.5%
91 920
 
3.4%
34 827
 
3.1%
92 697
 
2.6%
73 675
 
2.5%
19 659
 
2.5%
Other values (97) 8982
33.4%
ValueCountFrequency (%)
0 338
 
1.3%
1 219
 
0.8%
2 283
 
1.1%
3 6276
23.4%
4 1217
 
4.5%
5 30
 
0.1%
6 299
 
1.1%
7 595
 
2.2%
8 215
 
0.8%
9 63
 
0.2%
ValueCountFrequency (%)
106 1
 
< 0.1%
105 1
 
< 0.1%
104 442
 
1.6%
103 1
 
< 0.1%
102 1
 
< 0.1%
101 1
 
< 0.1%
100 10
 
< 0.1%
99 3128
11.6%
98 1
 
< 0.1%
97 324
 
1.2%

Weather Main
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5820923
Minimum0
Maximum6
Zeros2791
Zeros (%)10.4%
Negative0
Negative (%)0.0%
Memory size210.0 KiB
2024-02-21T18:00:21.363695image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.6180364
Coefficient of variation (CV)1.0227193
Kurtosis1.7512821
Mean1.5820923
Median Absolute Deviation (MAD)0
Skewness1.7736063
Sum42495
Variance2.6180419
MonotonicityNot monotonic
2024-02-21T18:00:22.144316image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 19281
71.8%
0 2791
 
10.4%
5 1713
 
6.4%
6 1524
 
5.7%
4 1180
 
4.4%
2 328
 
1.2%
3 43
 
0.2%
ValueCountFrequency (%)
0 2791
 
10.4%
1 19281
71.8%
2 328
 
1.2%
3 43
 
0.2%
4 1180
 
4.4%
5 1713
 
6.4%
6 1524
 
5.7%
ValueCountFrequency (%)
6 1524
 
5.7%
5 1713
 
6.4%
4 1180
 
4.4%
3 43
 
0.2%
2 328
 
1.2%
1 19281
71.8%
0 2791
 
10.4%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2.0
10287 
0.0
9233 
1.0
7209 
3.0
 
131

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters80580
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.0 10287
38.3%
0.0 9233
34.4%
1.0 7209
26.8%
3.0 131
 
0.5%

Length

2024-02-21T18:00:22.337507image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-21T18:00:22.702619image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
2.0 10287
38.3%
0.0 9233
34.4%
1.0 7209
26.8%
3.0 131
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 36093
44.8%
. 26860
33.3%
2 10287
 
12.8%
1 7209
 
8.9%
3 131
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 53720
66.7%
Other Punctuation 26860
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 36093
67.2%
2 10287
 
19.1%
1 7209
 
13.4%
3 131
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 26860
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 80580
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 36093
44.8%
. 26860
33.3%
2 10287
 
12.8%
1 7209
 
8.9%
3 131
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80580
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 36093
44.8%
. 26860
33.3%
2 10287
 
12.8%
1 7209
 
8.9%
3 131
 
0.2%

Weekday of Departure
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0577439
Minimum0
Maximum6
Zeros3492
Zeros (%)13.0%
Negative0
Negative (%)0.0%
Memory size210.0 KiB
2024-02-21T18:00:23.080638image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0080022
Coefficient of variation (CV)0.65669404
Kurtosis-1.2853291
Mean3.0577439
Median Absolute Deviation (MAD)2
Skewness-0.029638291
Sum82131
Variance4.0320726
MonotonicityNot monotonic
2024-02-21T18:00:23.305174image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 4370
16.3%
5 4209
15.7%
3 3994
14.9%
6 3981
14.8%
4 3610
13.4%
0 3492
13.0%
2 3204
11.9%
ValueCountFrequency (%)
0 3492
13.0%
1 4370
16.3%
2 3204
11.9%
3 3994
14.9%
4 3610
13.4%
5 4209
15.7%
6 3981
14.8%
ValueCountFrequency (%)
6 3981
14.8%
5 4209
15.7%
4 3610
13.4%
3 3994
14.9%
2 3204
11.9%
1 4370
16.3%
0 3492
13.0%

Weather Severity
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1.0
23303 
0.0
3557 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters80580
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 23303
86.8%
0.0 3557
 
13.2%

Length

2024-02-21T18:00:23.558792image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-21T18:00:23.788302image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 23303
86.8%
0.0 3557
 
13.2%

Most occurring characters

ValueCountFrequency (%)
0 30417
37.7%
. 26860
33.3%
1 23303
28.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 53720
66.7%
Other Punctuation 26860
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30417
56.6%
1 23303
43.4%
Other Punctuation
ValueCountFrequency (%)
. 26860
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 80580
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30417
37.7%
. 26860
33.3%
1 23303
28.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80580
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30417
37.7%
. 26860
33.3%
1 23303
28.9%

Season
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
20440 
1.0
6420 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters80580
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 20440
76.1%
1.0 6420
 
23.9%

Length

2024-02-21T18:00:23.963366image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-21T18:00:24.130955image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 20440
76.1%
1.0 6420
 
23.9%

Most occurring characters

ValueCountFrequency (%)
0 47300
58.7%
. 26860
33.3%
1 6420
 
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 53720
66.7%
Other Punctuation 26860
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 47300
88.0%
1 6420
 
12.0%
Other Punctuation
ValueCountFrequency (%)
. 26860
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 80580
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 47300
58.7%
. 26860
33.3%
1 6420
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80580
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 47300
58.7%
. 26860
33.3%
1 6420
 
8.0%

Visibility
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
22400 
1.0
4460 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters80580
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 22400
83.4%
1.0 4460
 
16.6%

Length

2024-02-21T18:00:24.254289image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-21T18:00:24.394600image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 22400
83.4%
1.0 4460
 
16.6%

Most occurring characters

ValueCountFrequency (%)
0 49260
61.1%
. 26860
33.3%
1 4460
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 53720
66.7%
Other Punctuation 26860
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 49260
91.7%
1 4460
 
8.3%
Other Punctuation
ValueCountFrequency (%)
. 26860
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 80580
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 49260
61.1%
. 26860
33.3%
1 4460
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80580
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 49260
61.1%
. 26860
33.3%
1 4460
 
5.5%

Interactions

2024-02-21T18:00:11.059605image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2024-02-21T17:59:29.050400image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2024-02-21T17:59:46.922546image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T17:59:50.929118image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T17:59:55.465400image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T17:59:57.737179image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T18:00:01.832865image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T18:00:04.469112image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T18:00:06.717809image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T18:00:10.601904image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T18:00:14.463495image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T17:59:28.745417image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T17:59:31.529364image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T17:59:34.403310image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T17:59:37.759237image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T17:59:41.124640image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T17:59:44.532074image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T17:59:47.158846image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T17:59:51.243671image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T17:59:55.592576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T17:59:57.885089image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T18:00:02.068289image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T18:00:04.625171image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T18:00:06.862661image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-02-21T18:00:10.865964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2024-02-21T18:00:24.528759image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Departure Delay (min)TemperatureFeels LikePressureHumidityWind SpeedWind DegreeCloudsRain 1hSnow 1hDeparture GateArrival IATA CodeAirline NameWeather MainWeekday of DepartureDeparture Time of DayWeather SeveritySeasonVisibility
Departure Delay (min)1.000-0.105-0.114-0.0400.0070.0720.0180.027-0.0020.097-0.022-0.0380.0200.054-0.0210.0050.0180.0450.017
Temperature-0.1051.0000.973-0.329-0.028-0.1220.0550.0970.212-0.1380.003-0.004-0.0040.098-0.0730.1520.1630.5590.200
Feels Like-0.1140.9731.000-0.241-0.067-0.3140.0400.0380.186-0.1770.004-0.004-0.0040.027-0.0810.1450.1060.6010.136
Pressure-0.040-0.329-0.2411.000-0.358-0.338-0.093-0.384-0.349-0.1700.013-0.006-0.001-0.4870.0710.0840.4450.1790.457
Humidity0.007-0.028-0.067-0.3581.0000.180-0.1800.3240.3290.2250.0070.017-0.0350.4820.0460.2210.4650.1940.543
Wind Speed0.072-0.122-0.314-0.3380.1801.0000.0540.2430.1360.193-0.0020.0010.0040.3000.0210.0720.2660.2490.293
Wind Degree0.0180.0550.040-0.093-0.1800.0541.000-0.184-0.033-0.175-0.010-0.0040.004-0.210-0.0210.0730.1800.2130.201
Clouds0.0270.0970.038-0.3840.3240.243-0.1841.0000.2220.204-0.0100.0010.0130.5820.0130.0820.2770.1660.297
Rain 1h-0.0020.2120.186-0.3490.3290.136-0.0330.2221.000-0.0700.0010.002-0.0060.495-0.0800.0730.5420.1030.472
Snow 1h0.097-0.138-0.177-0.1700.2250.193-0.1750.204-0.0701.000-0.0070.0020.0030.5070.0030.0810.4040.0800.338
Departure Gate-0.0220.0030.0040.0130.007-0.002-0.010-0.0100.001-0.0071.000-0.5770.157-0.007-0.0010.1420.0000.0670.000
Arrival IATA Code-0.038-0.004-0.004-0.0060.0170.001-0.0040.0010.0020.002-0.5771.000-0.1030.0020.0040.1570.0000.0380.012
Airline Name0.020-0.004-0.004-0.001-0.0350.0040.0040.013-0.0060.0030.157-0.1031.0000.013-0.0150.2180.0120.0570.000
Weather Main0.0540.0980.027-0.4870.4820.300-0.2100.5820.4950.507-0.0070.0020.0131.000-0.0720.0600.9630.2131.000
Weekday of Departure-0.021-0.073-0.0810.0710.0460.021-0.0210.013-0.0800.003-0.0010.004-0.015-0.0721.0000.0100.2020.1190.203
Departure Time of Day0.0050.1520.1450.0840.2210.0720.0730.0820.0730.0810.1420.1570.2180.0600.0101.0000.0150.0240.000
Weather Severity0.0180.1630.1060.4450.4650.2660.1800.2770.5420.4040.0000.0000.0120.9630.2020.0151.0000.0360.829
Season0.0450.5590.6010.1790.1940.2490.2130.1660.1030.0800.0670.0380.0570.2130.1190.0240.0361.0000.016
Visibility0.0170.2000.1360.4570.5430.2930.2010.2970.4720.3380.0000.0120.0001.0000.2030.0000.8290.0161.000

Missing values

2024-02-21T18:00:14.714831image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-21T18:00:15.153753image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Departure Delay (min)TemperatureFeels LikePressureHumidityWind SpeedWind DegreeCloudsRain 1hSnow 1hDeparture GateArrival IATA CodeAirline NameWeather MainDeparture Time of DayWeekday of DepartureWeather SeveritySeasonVisibility
0-0.177559-0.589572-0.4513840.638452-1.848781-0.665376-1.498219-1.818275-0.197941-0.17908941.024.00.01.01.00.01.01.00.0
10.120380-0.589572-0.4513840.638452-1.848781-0.665376-1.498219-1.818275-0.197941-0.17908943.04.031.01.01.00.01.01.00.0
22.563484-0.589572-0.4513840.638452-1.848781-0.665376-1.498219-1.818275-0.197941-0.17908928.065.023.01.01.00.01.01.00.0
30.030999-0.589572-0.4513840.638452-1.848781-0.665376-1.498219-1.818275-0.197941-0.17908939.065.072.01.01.00.01.01.00.0
4-0.237147-0.589572-0.4513840.638452-1.848781-0.665376-1.498219-1.818275-0.197941-0.17908969.021.04.01.01.00.01.01.00.0
50.477908-0.589572-0.4513840.638452-1.848781-0.665376-1.498219-1.818275-0.197941-0.17908927.062.092.01.01.00.01.01.00.0
6-0.833026-0.589572-0.4513840.638452-1.848781-0.665376-1.498219-1.818275-0.197941-0.17908959.08.099.01.01.00.01.01.00.0
7-0.505292-0.628954-0.6066620.540930-1.848781-0.334404-1.062101-1.327380-0.197941-0.17908925.061.03.01.01.00.01.01.00.0
8-0.296735-0.628954-0.6066620.540930-1.848781-0.334404-1.062101-1.327380-0.197941-0.17908956.023.099.01.01.00.01.01.00.0
9-0.237147-0.628954-0.6066620.540930-1.848781-0.334404-1.062101-1.327380-0.197941-0.17908971.014.094.01.01.00.01.01.00.0
Departure Delay (min)TemperatureFeels LikePressureHumidityWind SpeedWind DegreeCloudsRain 1hSnow 1hDeparture GateArrival IATA CodeAirline NameWeather MainDeparture Time of DayWeekday of DepartureWeather SeveritySeasonVisibility
268500.448114-1.583662-1.782920-0.2392450.0808070.9541780.7297770.665077-0.197941-0.17908940.030.03.01.01.02.01.01.00.0
268510.477908-1.583662-1.782920-0.2392450.0808070.9541780.7297770.665077-0.197941-0.17908956.020.099.01.01.02.01.01.00.0
26852-0.475498-1.646911-1.544421-0.141723-1.761073-0.5241610.3979480.231935-0.197941-0.17908950.014.03.01.01.00.01.01.00.0
268530.507702-1.673166-1.468304-0.141723-0.270027-0.7227440.8245850.636201-0.197941-0.17908950.051.03.01.00.02.01.01.00.0
26854-0.237147-1.583662-1.782920-0.2392450.0808070.9541780.7297770.665077-0.197941-0.17908950.023.03.01.01.02.01.01.00.0
268550.835435-2.130232-2.138132-0.2392451.133309-0.2505580.502237-0.489970-0.197941-0.17908945.028.013.01.01.00.01.01.00.0
268561.818635-1.917810-2.067089-0.239245-1.1471130.9541780.8245850.665077-0.197941-0.17908950.065.03.01.00.02.01.01.00.0
26857-0.862820-1.756703-1.930079-0.239245-0.3577361.1836520.9193940.665077-0.197941-0.17908950.055.074.01.00.02.01.01.00.0
268580.358732-1.756703-1.930079-0.239245-0.3577361.1836520.9193940.665077-0.197941-0.17908950.053.03.01.00.02.01.01.00.0
268590.239556-1.583662-1.782920-0.2392450.0808070.9541780.7297770.665077-0.197941-0.17908950.023.03.01.01.02.01.01.00.0

Duplicate rows

Most frequently occurring

Departure Delay (min)TemperatureFeels LikePressureHumidityWind SpeedWind DegreeCloudsRain 1hSnow 1hDeparture GateArrival IATA CodeAirline NameWeather MainDeparture Time of DayWeekday of DepartureWeather SeveritySeasonVisibility# duplicates
0-0.86282-2.182741-2.2893500.9310181.2210180.027458-1.4982190.260811-0.197941-0.17908962.012.099.01.00.05.01.01.00.02
1-0.86282-1.790117-1.9584960.6384521.0456010.314300-1.460296-1.500637-0.197941-0.17908960.029.013.01.02.05.01.01.00.02
2-0.86282-1.714934-1.5363020.4434081.571852-0.674202-1.4887380.636201-0.197941-0.17908913.049.04.01.02.06.01.01.00.02
3-0.86282-1.712547-1.1821050.3458861.571852-1.137562-1.1758710.665077-0.197941-0.17908938.036.07.01.02.06.01.01.00.02
4-0.86282-1.309183-1.4469910.931018-0.5331530.226041-1.3654870.549573-0.197941-0.17908924.058.069.01.00.05.01.01.00.02
5-0.86282-1.242354-1.3252050.833496-0.6208620.018632-1.3275640.462944-0.197941-0.17908932.037.04.01.00.05.01.01.00.02
6-0.86282-1.242354-1.3252050.833496-0.6208620.018632-1.3275640.462944-0.197941-0.17908965.032.099.01.00.05.01.01.00.02
7-0.86282-1.174331-1.4348130.735974-1.0594041.183652-1.4508150.665077-0.197941-0.17908950.056.072.01.02.05.01.01.00.02
8-0.86282-1.174331-1.4348130.735974-1.0594041.183652-1.4508150.665077-0.197941-0.17908962.023.019.01.02.05.01.01.00.02
9-0.86282-1.138529-0.9547700.833496-0.620862-0.722744-1.3086020.491820-0.197941-0.17908937.02.059.01.01.05.01.01.00.02